{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tanh(x):\n",
    "    return np.tanh(x)\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-1*x))\n",
    "\n",
    "def leak_relu(data, epsilon=0.1):\n",
    "    return np.maximum(epsilon * data, data)\n",
    "    \n",
    "def relu(data):\n",
    "    return np.maximum(0, data)\n",
    "    \n",
    "def Linear1(x, W=1.5):\n",
    "    return W*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(-2, 2, 0.01)\n",
    "\n",
    "y_linear1 = Linear1(x)\n",
    "y_sigmoid = sigmoid(x)\n",
    "y_tanh = tanh(x)\n",
    "y_leak_relu = leak_relu(x)\n",
    "y_relu = relu(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R4PokDrvL4ZoZUPxJbO7yo+C206CJRVdXJtuyZQtt2+qWr5JdEvq3obsvcvfF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\niEhIgSAiIoACQUREQv8f2PBIyB44uWwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fa50128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y_linear1, color = '#1797ff',linewidth=3)\n",
    "ax=plt.gca()\n",
    "ax.set_xlim([-2, 2])\n",
    "ax.set_ylim([-2, 2])\n",
    "ax.axhline(0, linestyle='--', color='gray', linewidth=1)\n",
    "ax.axvline(0, linestyle='--', color='gray', linewidth=1)\n",
    "plt.title(\"Linear\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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koDArkOsorBU5KMwK5DoKa0UOCjMzy1VTUEj6j5I2SzolqeI8rJKul/SipC2S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\na/bKSeIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fc395c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y_sigmoid, color = '#1797ff', linewidth=3)\n",
    "ax=plt.gca()\n",
    "ax.set_xlim([-2, 2])\n",
    "ax.set_ylim([-1, 1])\n",
    "ax.axhline(0, linestyle='--', color='gray', linewidth=1)\n",
    "ax.axvline(0, linestyle='--', color='gray', linewidth=1)\n",
    "plt.title(\"sigmoid\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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w5shYw5IWIpJEYWZjzWy5ma00syn17P+SmS02s3fN7DUzOzVh3+pw+9tmVhxFPCL5pOog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\nJnNoHEtyzgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109e10a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y_tanh, color = '#1797ff', linewidth=3)\n",
    "ax=plt.gca()\n",
    "ax.set_xlim([-2, 2])\n",
    "ax.set_ylim([-1.1, 1.1])\n",
    "ax.axhline(0, linestyle='--', color='gray', linewidth=1)\n",
    "ax.axvline(0, linestyle='--', color='gray', linewidth=1)\n",
    "plt.title(\"tanh\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6ECQfKRBEQigpKYm7BJHIKRBEQpg8eXLcJYhEToEgIiKAAkFERAIKBBERARQI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\nH1ng0iFgHTtvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109f16710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y_leak_relu, color = '#1797ff', linewidth=3)\n",
    "ax=plt.gca()\n",
    "ax.set_xlim([-2, 2])\n",
    "ax.set_ylim([-0.5, 2])\n",
    "ax.axhline(0, linestyle='--', color='gray', linewidth=1)\n",
    "ax.axvline(0, linestyle='--', color='gray', linewidth=1)\n",
    "plt.title(\"leak ReLU\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eOAcaKwzyil6oiSSgOYTq+8u7cP3sTN2nQ/SZBs2Kw/UkFVMgiEitmbQUrn4ePK6PaQeP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109f49518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y_relu, color = '#1797ff', linewidth=3)\n",
    "ax=plt.gca()\n",
    "ax.set_xlim([-2, 2])\n",
    "ax.set_ylim([-0.5, 2])\n",
    "ax.axhline(0, linestyle='--', color='gray', linewidth=1)\n",
    "ax.axvline(0, linestyle='--', color='gray', linewidth=1)\n",
    "plt.title(\"ReLU\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def softmax(x):\n",
    "    return np.exp(x) / np.sum(np.exp(x))\n",
    "\n",
    "y_softmax = softmax(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fBMs2h/flm2HLntzWP7IiJIOTDoWJQ2Bk3/yeMmrN8yBEioUShOTdh3vhjS2h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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10a09f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y_softmax, color = '#1797ff', linewidth=3)\n",
    "ax=plt.gca()\n",
    "ax.set_xlim([-2, 2])\n",
    "ax.set_ylim([-0.001, 0.01])\n",
    "ax.axhline(0, linestyle='--', color='gray', linewidth=1)\n",
    "ax.axvline(0, linestyle='--', color='gray', linewidth=1)\n",
    "plt.title(\"Softmax\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
